Method and system for training a target machine learning model for a target system

Abstract

A method for training a target machine learning model for a target system in engineered processes and machines. A multitask Gaussian process implements a joint model of safety values of the target and auxiliary system. A new state is selected for the target system, wherein target safety values are predicted by the multitask Gaussian process.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 training a target machine learning model for a target system in engineered processes and machines, the target system being configurable into one of multiple possible states, in operation, the target system allowing measurement of: (i) at least one target safety value of the target system, a configuration of the target system being defined as safe by the at least one target safety values lying in a safe region, and (ii) a physical quantity for monitoring or controlling the target system, the target machine learning model being configured to receive as input a state of the target system and to produce as output a prediction of the physical quantity, the training the target machine learning model including the following steps:
 obtaining auxiliary training data corresponding to an auxiliary system, the auxiliary training data including multiple pairs of an auxiliary state and auxiliary safety values; 
 initializing a multitask Gaussian process implementing a joint model of safety values of the target system and the auxiliary system, the multitask Gaussian process taking as input a state, the state being a state of the auxiliary system or of the target system, and producing a prediction for a target safety value or auxiliary safety value respectively; and 
 iteratively training the target machine learning model including:
 selecting a state from the multiple possible states for the target system, wherein target safety values predicted by the multitask Gaussian process for the selected state lie in the safe region, 
 obtaining the physical quantity and at least one target safety value for the selected state in the target system, including configuring the target system according to the selected state, the physical quantity and the safety values being measured through a sensor, 
 updating the multitask Gaussian process with the selected state and corresponding target safety values, and 
 updating the target machine learning model with the selected state and the physical quantity. 
 
   
     
     
         2 . The method as in  claim 1 , wherein the target system is a vehicle or robotic device, the target system being configured for at least partial autonomous movement. 
     
     
         3 . The method as in  claim 1 , wherein the auxiliary system is a computer simulation of the target system. 
     
     
         4 . The method as in  claim 1 , wherein the target system and the auxiliary system each include an engineered process of a same type or a machine of a same type, such that measurements from the auxiliary system are predictive of the target system. 
     
     
         5 . The method as in  claim 1 , wherein the target machine learning model includes a multitask Gaussian process, wherein the auxiliary training data include auxiliary physical quantities for the auxiliary states in the auxiliary training data, and the method further comprises: initializing a multitask Gaussian process implementing a joint model of a physical quantity of the target system and the auxiliary system, the multitask Gaussian process taking as input a state, the state being a state of the auxiliary system or of the target system, and producing a prediction for a target physical quantity or an auxiliary physical quantity respectively. 
     
     
         6 . The method as in  claim 1 , wherein the multitask Gaussian process has a part related to the auxiliary system, wherein updating the multitask Gaussian process with the selected state and the target safety values leaves the part of the multitask Gaussian process related to the auxiliary system unchanged. 
     
     
         7 . The method as in  claim 1 , wherein selecting a state of the multiple possible states includes optimizing an acquisition function. 
     
     
         8 . The method as in  claim 1 , wherein the target system allows measurement of at least one target safety value out of: a Temperature, Pressure, Vibration Level, Noise Level, Humidity, Current or Voltage Level, Flow Rate, Force, Torque, Speed, RPM, Chemical Concentration, Charge State. 
     
     
         9 . The method as in  claim 1 , wherein target system allows measurement of at least one physical quantity out of:
 (i) at least one of: a Temperature, Pressure, Vibration Level, Noise Level, Humidity, Current or Voltage Level, Flow Rate, Force, Torque, Speed, RPM, Chemical Concentration, Charge State, or   (ii) a position of an obstacle near the target machine.   
     
     
         10 . The method as in  claim 1 , further comprising:
 obtaining the state of the target system; and   applying the target machine learning model to the obtained state to obtaining as output a prediction of the physical quantity.   
     
     
         11 . The method as in  claim 10 , further comprising:
 (i) monitoring the target system, wherein the obtained state is a state in which the target system is configured, and testing wherein the predicted physical quantity falls outside a desired range, and starting a recovery if not, and/or   (ii) controlling the target system, wherein multiple states are obtained, the target machine learning model being applied to each of the multiple states thus obtaining as output a prediction of the physical quantity for each of the multiple states, selecting a state from the multiple states in dependence on the predicted physical quantity, and configuring the target system according to the state.   
     
     
         12 . The method as in  claim 11 , wherein the target system includes an at least partially autonomous vehicle, the state includes: (i) one or more of a control state including a combination of one or more of: throttle, brake, steering angle; (ii) a vehicle state including a combination of one or more of: a position, an orientation, a longitudinal velocity, and a lateral velocity of the vehicle, a gearbox position, an engine RPM; and (iii) a road state including a combination of one or more of: surrounding objects and traffic, and road information; wherein the machine learning output includes a predicted change in the vehicle state. 
     
     
         13 . A system comprising:
 one or more processors; and   one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to perform:
 training a target machine learning model for a target system in engineered processes and machines, the target system being configurable into one of multiple possible states, in operation, the target system allowing measurement of: (i) at least one target safety value of the target system, a configuration of the target system being defined as safe by the at least one target safety values lying in a safe region, and (ii) a physical quantity for monitoring or controlling the target system, the target machine learning model being configured to receive as input a state of the target system and to produce as output a prediction of the physical quantity, the training the target machine learning model including the following steps:
 obtaining auxiliary training data corresponding to an auxiliary system, the auxiliary training data including multiple pairs of an auxiliary state and auxiliary safety values; 
 initializing a multitask Gaussian process implementing a joint model of safety values of the target system and the auxiliary system, the multitask Gaussian process taking as input a state, the state being a state of the auxiliary system or of the target system, and producing a prediction for a target safety value or auxiliary safety value respectively; and 
 iteratively training the target machine learning model including:
 selecting a state from the multiple possible states for the target system, wherein target safety values predicted by the multitask Gaussian process for the selected state lie in the safe region, 
 obtaining the physical quantity and at least one target safety value for the selected state in the target system, including configuring the target system according to the selected state, the physical quantity and the safety values being measured through a sensor, 
 updating the multitask Gaussian process with the selected state and corresponding target safety values, and 
 updating the target machine learning model with the selected state and the physical quantity. 
 
 
   
     
     
         14 . A non-transitory computer readable medium data representing instructions, which when executed by a processor system, cause the processor system to perform:
 training a target machine learning model for a target system in engineered processes and machines, the target system being configurable into one of multiple possible states, in operation, the target system allowing measurement of: (i) at least one target safety value of the target system, a configuration of the target system being defined as safe by the at least one target safety values lying in a safe region, and (ii) a physical quantity for monitoring or controlling the target system, the target machine learning model being configured to receive as input a state of the target system and to produce as output a prediction of the physical quantity, the training the target machine learning model including the following steps:
 obtaining auxiliary training data corresponding to an auxiliary system, the auxiliary training data including multiple pairs of an auxiliary state and auxiliary safety values; 
 initializing a multitask Gaussian process implementing a joint model of safety values of the target system and the auxiliary system, the multitask Gaussian process taking as input a state, the state being a state of the auxiliary system or of the target system, and producing a prediction for a target safety value or auxiliary safety value respectively; and 
 iteratively training the target machine learning model including:
 selecting a state from the multiple possible states for the target system, wherein target safety values predicted by the multitask Gaussian process for the selected state lie in the safe region, 
 obtaining the physical quantity and at least one target safety value for the selected state in the target system, including configuring the target system according to the selected state, the physical quantity and the safety values being measured through a sensor, 
 updating the multitask Gaussian process with the selected state and corresponding target safety values, and 
 updating the target machine learning model with the selected state and the physical quantity.